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Reinforcement Learning of Coordination in Heterogeneous Cooperative Multi-agent Systems [chapter]

Spiros Kapetanakis, Daniel Kudenko
2005 Lecture Notes in Computer Science  
In this short paper we investigate the problem of learning to coordinate with heterogeneous agents.  ...  This makes ef fective coordination particularly dif ficult to learn, especially in the absence of learning agent standards.  ...  Conclusions and Outlook We have presented a proof of concept study of the learning of coordination for heterogeneous multi-agent systems.  ... 
doi:10.1007/978-3-540-32274-0_8 fatcat:k2wfnkvhfncahmjierb4re3qb4

Heterogeneous Agent Cooperative Planning Based on Q-Learning

Chenfeng Gan, Wuhan Institute of Technology, Wuhan, China, Wei Liu, Ning Wang, Xingyu Ye
2021 Journal of clean energy technologies  
In this paper, we present a model to achieve the collaboration of heterogeneous agent in the open-dynamic environment.  ...  Heterogeneous rescue agent is used to assist agent in the scene.  ...  Research on agent collaboration based on reinforcement Learning includes the multi-crawler system of the Internet [12] , research on Q-learning algorithm's multi-agent planning [13] and multi-agent  ... 
doi:10.7763/ijcte.2021.v13.1284 fatcat:uevmze3syzd5ro3saxvwfobjhi

Multi-Robot Cooperation Strategy in Game Environment Using Deep Reinforcement Learning

Hongda Zhang, Decai Li, Yuqing He
2018 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO)  
Furthermore, we conducted experiments on the actual multi-robot system platform and demonstrated the feasibility of multi-agent cooperation strategy in practical multi-robot system based on deep reinforcement  ...  The research progress of multi-agent decision-making strategies in the game environment based on deep reinforcement learning provides a solution for solving the problems faced by multi-robot systems.  ...  In the second part of the paper, we review the multi-agent cooperation strategy research in the game environment based on deep reinforcement learning and its application on multi-robot systems.  ... 
doi:10.1109/robio.2018.8665165 dblp:conf/robio/ZhangLH18 fatcat:z6dsgnkvsbgvtcfcmrqqyci72i

Cooperative Multi-Agent Learning: The State of the Art

Liviu Panait, Sean Luke
2005 Autonomous Agents and Multi-Agent Systems  
In this survey we attempt to draw from multi-agent learning work in a spectrum of areas, including reinforcement learning, evolutionary computation, game theory, complex systems, agent modeling, and robotics  ...  Cooperative multi-agent systems are ones in which several agents attempt, through their interaction, to jointly solve tasks or to maximize utility.  ...  Along with multi-agent systems in general, multi-agent learning is at the cusp of a major growth spurt.  ... 
doi:10.1007/s10458-005-2631-2 fatcat:u3xlftotajfitdtfmvbmggwgbi

Multi-Robot Information Fusion and Coordination Based on Agent [chapter]

Bo Fan, Jiexin Pu
2011 Multi-Robot Systems, Trends and Development  
Multi-agent coordination based on reinforcement learning In this section, the multi-agent coordination based on distributed reinforcement learning is proposed, which is shown in Figure 11 .  ...  In this section, a multi-agent coordination based on distributed reinforcement learning is proposed.  ...  Multi-robot Information Fusion and Coordination Based on Agent 365 In member level, team games focus on the cooperation of member agents.  ... 
doi:10.5772/13029 fatcat:yg3uyhyb5vc7fb3jq7tzboyoh4

Transfer Learning Method Using Ontology for Heterogeneous Multi-agent Reinforcement Learning

Hitoshi Kono, Akiya Kamimura, Kohji Tomita, Yuta Murata, Tsuyoshi Suzuki
2014 International Journal of Advanced Computer Science and Applications  
In MARS, autonomous agents obtain behavior autonomously through multi-agent reinforcement learning and the transfer learning method enables the reuse of the knowledge of other robots' behavior, such as  ...  A multiagent robot system (MARS) that utilizes reinforcement learning and a transfer learning method has recently been studied in realworld situations.  ...  ACKNOWLEDGMENT This work was partially supported by the Research Institute for Science and Technology of Tokyo Denki University Grant Number Q14J-01 Japan.  ... 
doi:10.14569/ijacsa.2014.051022 fatcat:iavbukidujayzp3q33izppabr4

Multi-robot concurrent learning of cooperative behaviours for the tracking of multiple moving targets

Zheng Liu, Marcelo H. Ang Jr, Winston Khoon Guan Seah
2006 International Journal of Vehicle Autonomous Systems  
Reinforcement learning has been extensively studied and applied for generating cooperative behaviours in multi-robot systems.  ...  Furthermore, to address the problems in concurrent learning, we propose a distributed learning control algorithm to coordinate the concurrent learning processes.  ...  In other words, for some cooperative multi-robot systems, the 'optimisation' lies in the relationship among robots.  ... 
doi:10.1504/ijvas.2006.012207 fatcat:6rrx7r5fh5gghdpphj2e73fbbe

Reinforcement learning of cooperative behaviors for multi-robot tracking of multiple moving targets

Zheng Liu, M.H. Ang, W.K.G. Seah
2005 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems  
Index Terms-Reinforcement learning; concurrent learning; behavior based control; multi-robot cooperation.  ...  In addition, to address the problems in concurrent learning, a distributed learning control algorithm is proposed to coordinate concurrent learning processes.  ...  In last two decades, reinforcement learning has been extensively studied for multi-robot concurrent learning of cooperative behaviors.  ... 
doi:10.1109/iros.2005.1545146 dblp:conf/iros/LiuAS05 fatcat:sosemm7k2fc2beuwbxkzahkgkm

HAMMER: Multi-Level Coordination of Reinforcement Learning Agents via Learned Messaging [article]

Nikunj Gupta, G Srinivasaraghavan, Swarup Kumar Mohalik, Matthew E. Taylor
2021 arXiv   pre-print
Cooperative multi-agent reinforcement learning (MARL) has achieved significant results, most notably by leveraging the representation learning abilities of deep neural networks.  ...  After explaining our MARL algorithm, hammer, and where it would be most applicable, we implement it in the cooperative navigation and multi-agent walker domains.  ...  has taken place in the Intelligent Robot Learning (IRL) Lab at the University of Alberta, which is supported in part by research grants from the Alberta Machine Intelligence Institute (Amii), CIFAR, and  ... 
arXiv:2102.00824v1 fatcat:u3deetdxwvh6vffrqvueqto2xa


Ginni Devi
2018 International Journal of Advanced Research in Computer Science  
Coordination in cooperative multi-agent systems is one of the important issues in multi-agent learning and has been broadly studies in the literature.  ...  In this work, we look over the multi-agent coordination problems in cooperative environments under the networked multi-agent learning framework using some social network structures and will try to improve  ...  They studied (a simple form of reinforcement learning) Q-learning in cooperative multi-agent systems under the two perspectives, the first one focused on the influence of that game structure including  ... 
doi:10.26483/ijarcs.v9i2.5818 fatcat:enbuczj4ffh5rgp7ksrxkp6dbq

Reinforcement Learning in Dynamic Task Scheduling: A Review

Chathurangi Shyalika, Thushari Silva, Asoka Karunananda
2020 SN Computer Science  
Especially in real-world dynamic systems where multiple agents involve in scheduling various dynamic tasks is a challenging issue.  ...  The paper addresses the results of the study by means of the state-of-theart on Reinforcement learning techniques used in dynamic task scheduling and a comparative review of those techniques.  ...  Compliance with ethical Standards Conflicts of Interest/Competing Interests The authors declare that there are no conflicts of interest regarding the publication of this article.  ... 
doi:10.1007/s42979-020-00326-5 fatcat:egp6vgpetbcwdasm45vunmo3n4

Cooperative Multi-Agent Reinforcement Learning for Multi-Component Robotic Systems: guidelines for future research

Manuel Graña, Borja Fernandez-Gauna, Jose Manuel Lopez-Guede
2011 Paladyn: Journal of Behavioral Robotics  
In this paper, we identify the main issues which offer opportunities to develop innovative solutions towards fully-scalable cooperative multi-agent systems.  ...  the exponential state space growth, coordination issues, and the propagation of rewards among agents.  ...  Cooperative Multi-Agent Reinforcement Algorithms In fully-cooperative systems, agents should coordinate to achieve the team goal and most authors consider a unique shared reward signal.  ... 
doi:10.2478/s13230-011-0017-5 fatcat:blzr2yqkbrdyfbkvolwyb2koge

Using distributed w-learning for multi-policy optimization in decentralized autonomic systems

Ivana Dusparic, Vinny Cahill
2009 Proceedings of the 6th international conference on Autonomic computing - ICAC '09  
Distributed W-Learning (DWL) is a reinforcement learningbased algorithm for multi-policy optimization in agent-based systems.  ...  In this poster we propose the use of DWL for decentralized multi-policy optimization in autonomic systems.  ...  The authors would like to thank thank Fabian Bustamante for his feedback on the previous draft of this paper, As'ad Salkham for his implementation of the RL libraries used as the basis for our DWL implementation  ... 
doi:10.1145/1555228.1555247 dblp:conf/icac/DusparicC09 fatcat:qdiypkaegba6dj3anj7g6nh3za

A Survey and Analysis of Cooperative Multi-Agent Robot Systems: Challenges and Directions [chapter]

Zool Hilmi Ismail, Nohaidda Sariff
2018 Applications of Mobile Robots [Working Title]  
Research in the area of cooperative multi-agent robot systems has received wide attention among researchers in recent years.  ...  Therefore, this paper reviewed various selected literatures primarily from recent conference proceedings and journals related to cooperation and coordination of multi-agent robot systems (MARS).  ...  Conflicts of interest Some works in this paper are based on study review from selected journals and proceedings regarding the cooperative multi-agent robot systems.  ... 
doi:10.5772/intechopen.79337 fatcat:ob2kmrbzcrekfcch7lby7l4wwy

Multiple Mobile Robot Systems [chapter]

Lynne E. Parker
2008 Springer Handbook of Robotics  
techniques in Chapter 9) and in multi-agent systems [92] , much less work has been done in the area of multi-robot learning, although the topic is gaining increased interest.  ...  Learning Multi-robot learning is the problem of learning new cooperative behaviors, or learning in the presence of other robots.  ... 
doi:10.1007/978-3-540-30301-5_41 fatcat:v3yo5joepja2lketepmahupkfm
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